An optical array imaging system is presented with basic experimental results. First, a remote object is illuminated with laser light at an angle and the reflected light is detected with an array sensor after interfering it with the reference light. This process is repeated by changing the illumination angle to collect a set of fringe patterns, which are A/D converted and stored in a harddisk in a computer. Then, the data are processed on a computer, first, to estimate the complex-amplitude object wave fields, second, to derive the eigenvector with the maximum eigenvalue for the correlation of the estimated object fields, and finally, to form an image of the object. The derivation of the eigenvector follows an iterative algorithm, which can be interpreted as the process of repeating backward wave propagation of the field between the two apertures illuminating and detecting laser light. The eigenvector field can be expected to backpropagate to focus at a point on the object with the maximum coefficient of reflection, so that a beam-steering operation is applied to the eigenvector to form an image of the object. The method uses only the information of the array data and the lateral spacings of the receiving array (CCD) elements. Hence, the method can give good images of objects even if the reference light is uncollimated with an unknown distorted wavefront, and even if the illuminating angles are imprecise in three dimensions. Basic experimental results clearly show the usefulness of the method.

An optical array imaging system has been presented in previous articles. In this system, first, the object is illuminated with laser light sequentially from each of the array elements and the reflected field is detected as interferogram. The interferograms obtained are then spatially heterodyne-detected on a computer to extract the signal components, that is, array data. Then, the eigenvector of the largest eigenvalue is derived by applying the power method to the array data and it is beam-steered to get images of the object. The algorithm gives good images for most objects, but it fails to work for some objects. It was shown that using a subset of the array data may solve the problem, but that finding the corresponding optimum subaperture is quite time-consuming. In this paper, first, the integral equation describing the system is solved for a general class of object, to make clear the conditions for the eigenvector to form a sharp beam. Second, the imaging algorithm is sped up to a great degree by optimizing only the illuminating aperture in a coarse fashion. Third, the rate of convergence of the power method is adaptively estimated in the algorithm to make the eigenvector derivation reliable. Finally the improved algorithm is investigated using both computer-generated and experimentally obtained array data.

In a previous article, an optical array imaging system has been presented. In this system, first, a set of array data is collected by repeatedly illuminating the object with laser light from each array element, detecting the reflected light as interferogram, and extracting the reflected wave field based on the spatial heterodyne detection. Then, an eigenvalue analysis is applied to the data to derive the wave field that would backpropagate and focus at a single point on the object; in this case, the iterative algorithm is used which indicates that the object point may have the largest reflectivity. It was shown experimentally that the single-point-focusing was attained for objects having several such parts with almost the same reflectivities. A preliminary study by computer simulation, however, indicates that the probability with which the wave focuses at multiple object points would not be small enough, resulting in a degraded image with ghost image components. In this paper, the array data within subaperture regions are selectively used to attain the single-point-focusing and obtain a good image for any object. First, it is shown analytically that the change in the dimension or center position of the aperture is effective to change the eigenvector so that it attains the single-point-focusing. Then, a procedure to find the optimum subapertures and a measure evaluating the degree of single-point-focusing for the eigenvector are presented. The method is examined in detail using experimentally obtained array data, and the results show that the method is effective in obtaining good images for any objects without sacrificing image resolution. When we compare the imaging system to an automatic focusing camera, it may be said that the additional processings enhance the capability of automatic focusing to a great degree.